Factors Affecting Commuters' Intentions in Using Park and Ride (P&R) Facilities Based on Theory of Planned Behavior
Bibliographic record
Abstract
Park and Ride (P&R) is a form of transportation demand management closely related to commuting activities. Several developed countries, such as the UK, Canada, China, and Hong Kong already implemented P&R with a high level of effectiveness and success in overcoming the congestion problems in the city center, low use of public transportation, and air pollution. However, in developing countries, the various positive impacts of P&R still have not been able to encourage commuters' intentions to use these facilities. The level of P&R use at Sidoarjo Station is still relatively low (44.3%). Behavioral is one of several keys to the success of P&R that depends on intention and ability. The intention is the result of knowledge, social, and infrastructure that can support the use of public transport and P&R. This study aims to identify factors that can influence commuters' intentions to use P&R at Sidoarjo Station based on the theory of planned behavior using SEM analysis. The results showed that P&R and public transportation conditions as perceived behavioral control were the most influential factors on commuter intentions. The conditions of public transportation (including availability and location) and the quality of P&R facilities are also essential considerations for commuters using P&R
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".